Authors:
Gabriele Martino
1
;
Federico Galatolo
1
;
Mario Cimino
1
and
Christian Callegari
2
Affiliations:
1
Dept. Information Engineering, University of Pisa, L.go Lazzarino 1, 56122, Pisa, Italy
;
2
Quantavis s.r.l., L.go Spadoni, 56126 Pisa, Italy
Keyword(s):
POS Malware, RAM Scraper, Anomaly Detection, Malware Traffic Data, Self-Attention, Transformer.
Abstract:
In recent years, electronic payment through Point-of-Sale (POS) systems has become popular. For this reason, POS devices are becoming more targeted by cyber attacks. In particular, RAM scraping malware is the most dangerous threat: the card data is extracted from the process memory, during the transaction and before the encryption, and sent to the attacker. This paper focuses on the possibility to detect this kind of malware through anomaly detection based on Deep Learning with attention, using the network traffic with data exfiltration occurrences. To show the effectiveness of the proposed approach, real POS transaction traffic has been used, together with real malware traffic extracted from a collection of RAM scrapers. Early results show the high potential of the proposed approach, encouraging further comparative research. To foster further development, the data and source code have been publicly released.